OICR[1]} continues the former method and adds a multi-stage classifier to complete more accurate positioning. WCCN[2]} completes the division of candidate regions through Class Activation Map (CAM)[3]} at first, the principle of CAM is that map prediction score on the previous features map to generate the class-specific heat map, and then use the second-stage model to screen better candidate regions. Methods such as ACoL[4]} and SPG[5]} are also based on CAM to complete localization. DANet[6]} learns more diverse features by comparing the fusion category and uses CAM to locate more complete objects. CASD[7]} trains OICR[1]} by means of self-distillation, and MIST[9]} perform self-training by generating virtual labels. The above methods show that category labels can provide features rich in object location and locate more class-discriminating regions. However, the task with WSOD is still not the same as with FGVC. The goal of the FGVC task is no longer to detect complete objects, but to find the most critical regions and exploit these regions for better recognition.
